1. Field of the Invention
The teachings herein relate to system prognosis in general, and fault detection and localization in particular.
2. Description of the Related Art
Abnormal condition detection is an important first step in system prognosis. Abnormal conditions, also known as faults, are the first sign of a potential equipment failure at some future time. Detecting abnormal conditions implies, at the very least, detecting change in time series data from one or more sensors. Having detected that there has been a change, one often further desires to precisely locate the time of the change, (e.g., to allow in a manufacturing system the removal of defective units).
The direct cost of equipment failures is unavoidable: ultimately, the faulted component must be replaced. However, there are indirect costs to equipment failure that are in many cases far greater than the cost of the repair. One source of indirect costs is secondary damage (e.g., component failure in the compressor stage of a gas turbine might cause damage to the rear stages). Another indirect cost is unscheduled maintenance. It can be far less expensive to replace a faulty component before it has failed during scheduled maintenance than to have a component fail and have to shut the system down unexpectedly. In addition, guaranteed uptime is often written into service contracts.
Avoidance of unscheduled downtime and costly secondary damage make the accurate detection of faults and prediction of equipment remaining useful life of enormous economic benefit to industry. The detection of faults is an important first step in building a prognostic reasoning system. Thus, there is considerable motivation to detect faults early and accurately in many systems.